Automatic recommender systems are often used to assist users in selecting items that fit their taste. From a large set of items that a person can choose from, a recommender system makes a selection that fits the taste of a given user.
Before a recommender system can give truly personalized recommendations, it will first have to learn the user's taste. For this, the user typically has to rate a number of items, e.g., specify the extent to which he likes or dislikes a number of items.
Recommender systems can be broadly divided into two categories, namely content-based recommender systems and collaborative filtering-based recommenders. For the first type, items have to be characterized by a number of features. For example, a movie can be characterized by the title, the genre, the director, the cast, etc. The rating history of a user (the specification of likes and dislikes of a number of items) can then be used to estimate the correlation between feature-values and the probability that the user will like an item with these feature-values. In contrast, a recommender system using collaborative filtering uses ratings of a large community of users to extract from this a similarity between users (because they like/dislike the same items) or a similarity between items (because they are liked/disliked by the same users). This information is then used to either recommend items that are similar to the items that the user has specified to like or recommend items that are liked by users that are similar to the given user (and are not yet watched or bought by the given user). Collaborative filtering approaches do not need a characterization of the items in terms of feature-values.
Over the last years, the popularity of social network services, such as Facebook to and LinkedIn, has increased considerably. These services support a user to easily exchange ideas, interests, etc. with friends, family, and colleagues. These services also offer users the possibility to express their interests by “liking” entities, such as movies, music, celebrities, organizations, products, etc. Each of these entities is specified by a webpage that gives further details of the specific entity. For example, Facebook has an extensive collection of these entities that can be searched for by users.
Suppose that a user wants to express his or her interests for a given entity. If a webpage already exists for this entity, then the user can simply press on the corresponding “like” button, and a link to this webpage will be added to the user's profile. If no suitable webpage expresses the entity of his or her interest, the user can create such a webpage by additionally adding textual information about the entity. For many entities, this information is extracted from Wikipedia or other sources, providing detailed high-quality information.
For both categories of recommender systems mentioned above, a user that is new to the recommender system first has to rate a number of items before the recommender can generate useful personalized recommendations. This may hamper the wide-spread use of a recommender system, since users may not always be willing to initially invest time and effort in “explaining” the system their taste. Still, users expect immediate recommendations. A recommender system will be able to learn the taste of a user over time, but in that case the recommendations will initially be not optimally tuned to the specific user.
One way to address this issue is to let the recommender system initially recommend items that are liked by many users. However, a critical user may not appreciate these recommendations as very valuable, and he or she may stop using the recommender system before it has been able to tune its recommendations.
Another approach is found in Chumki Basu ET AL: “Technical paper recommendation: A study in combining multiple information sources”, Journal of Artificial Intelligence Research 1, 1 Jan. 2001 (2001-01-01), pages 231-252. In this article the use of he WHIRL system is proposed to retrieve hits from multiple information sources.